Wireless connectivity will soon enable people to consume augmented and virtual reality content anywhere and cloud-connected mobile robots to perform complex tasks collaboratively. This connectivity will come to enterprises, factory floors and digital homes first, powered by indoor networks that offer much higher data rates, greater reliability, and lower latency than today’s networks. In addition to providing traditional communication capabilities, the future indoor network will have a “sixth sense” that enables it to provide sensory information and insights to meet physiological needs such as lighting, heating, health and safety. It will serve as the core infrastructure for smart buildings and help enterprises operate more efficiently by further enabling capabilities such as immersive virtual workplaces, indoor navigation and asset tracking. This paper reviews current technologies, examines the enabling technologies of the future indoor network and presents our latest research results and our vision for implementing them in commercial and residential environments.

The evolution of 5G and Beyond networks has enabled new applications with stringent end-to-end latency requirements, but providing reliable low-latency service with high throughput over public wireless networks is still a significant challenge. One of the possible ways to solve this is to exploit path diversity, encoding the information flow over multiple streams across parallel links. The challenge presented by this approach is the design of joint coding and scheduling algorithms that adapt to the state of links to take full advantage of path diversity. In this paper, we address this problem for a synchronous traffic source that generates data blocks at regular time intervals (e.g., a video with constant frame rate) and needs to deliver each block within a predetermined deadline. We first develop a closed-form performance analysis in the simple case of two parallel servers without any buffering and single-packet blocks, and propose a model for the general problem based on a Markov Decision Process (MDP). We apply policy iteration to obtain the coding and scheduling policy that maximizes the fraction of source blocks delivered within the deadline: our simulations show the drawbacks of different commonly applied heuristic solutions, drawing general design insights on the optimal policy.

Wireless backhaul communication and power provision to fifth-generation small cells (SCs) is expected to decrease their installation cost significantly. In this paper, hybrid solar/laser-based energy harvesting and storage are investigated for the self-sufficient year-round operation of outdoor SCs. The required electrical power for a SC is assumed to be 10 W according to state-of-the-art millimeter-wave phased-array transceivers. Here we show through simulations that hybrid solar/laser-based energy harvesting with storage and exclusive laser-based energy harvesting enable the SC operation not only in sunnier parts of the world but also in ‘darker’ places. Since the highest monthly power required from the laser is shown to be 10.1 W, a highly directive optical wireless link is designed using the simulation software Zemax. The operation wavelength of 1310 nm is selected because of the highest maximum permissible exposure of 1 W/cm2. The designed system is shown to be capable of harvesting 10.4 W at the distance of 100 m with transmitter-receiver dimensions of the order of cm3and meeting eye-safety regulations for Class 1M.

MPTCP is used in Multi-WAN Routers to aggregate multiple WAN/Internet connections using two architectural variants: proxying and tunneling. The proxy variant creates one MPTCP connection for each TCP connection, resulting in a large number of parallel uncoordinated MPTCP connections, which leads to underutilizating the available capacity, suboptimal scheduling, and increased loss rate. The tunnel variant encapsulates TCP over MPTCP, stacking two reliability layers, which leads to large number of spurious retransmissions, an issue known as TCP meltdown. We propose a new solution, BOOST, that eliminates the problems with both variants by multiplexing TCP connections over a single persistent multi-path connection. BOOST also takes a hybrid approach to multi-path scheduling combining load balancing and scheduling by transmitting short flows across a single path, avoiding HoL blocking, and opportunistically transmitting long flows across multiple path, utilizing left-over capacity. Evaluations show that BOOST provides better throughput, lower losses, and retransmissions compared to MPTCP

Next-generation wireless networks are expected to enable new applications with strict latency constraints. However, existing transport layer protocols are unable to meet the stringent Quality of Service (QoS) requirements on throughput and maximum latency: excessive queuing due to capacity-oriented congestion control inflates end-to-end latency well beyond interactivity deadlines. In this work, we propose a novel framework that evolves best-effort communications into reliability- and latency-aware communications for QoS-sensitive applications. The new protocol, named High-reliability latency-bounded Overlay Protocol (HOP), provides a novel combination of packet-level Forward Error Correction (FEC) and multipath scheduling to compensate for capacity drops and meet pre-defined QoS requirements. More specifically, the sender splits the data and the associated redundancy between the paths by using a stochastic forecast of their future capacity and decides the amount of redundancy necessary to meet the application’s requirements without clogging the connections. We compare HOP’s performance with state-of-the-art multipath protocols in ns-3 simulations using both synthetic and live network traces, and confirm that our scheme can reliably deliver high-throughput data, reducing the number of late blocks by 2 to 5 times with respect to optimized Multipath TCP (MPTCP).

Grammar-Guided Genetic Programming is already outperforming humans at creating efficient transmission schedulers for large heterogeneous communications networks. We have previously proposed a multi-level grammar approach which achieved significantly better results than the canonical Grammar-Guided Genetic Programming approach. Initially, a restricted `small' grammar is utilised in order to discover suitable structures. A full grammar is then adopted after this initial phase. Hence, evolution can focus on maximising performance, by fine-tuning the well-structured models. In this work, we propose to use a hierarchical approach by employing multiple small grammars instead of a unique small grammar at the lower level, in conjunction with the full grammar at the upper level. To use multiple small grammars while maintaining the same computational budget, we have to use either (i) reduce the number of generations, or (ii) reduce the size of the population for the evolution with each of the small grammars. In this work, we confirm that the hierarchical grammar approach using the division of number of generations strategy achieves significantly better results than the multi-level approach, but requires defining an ideal number of small grammars to achieve the best performance. We also show that the hierarchical grammar approach using the division of population size strategy achieves significantly better results than the multi-level approach. However the division of population size strategy is less sensitive to the number of small grammars.

Delivering massive video content while consuming low energy at the user devices is an issue of increasing importance, both for the users and operators. In this paper we consider the problem of minimum-energy video delivery when the users are served by LTE multicast transmissions which are assisted by Wi-Fi networks. We formulate the optimization problem where we jointly decide the user association and spectrum allocation to multicast groups, and the Wi-Fi access point (AP) association and airtime allocation to users, in order to deliver a requested video quality. The problem is NP-hard and we propose a cutting planes algorithm, based on Benders decomposition, to expedite its exact solution. We evaluate our proposal using a wealth of simulation experiments based on 3GPP parameters and measurement studies. Our findings show that coordinated decisions on both networks reduce the devices power consumption by up to approximately 50%.

360° video streaming is rapidly progressing towards the multimedia industry, offering an interactive user experience but also challenging telco operators due to the huge volume of the video data and the user channel dynamics. We propose a software defined transport layer proxy overlay architecture, that builds on top of existing standard technologies, to facilitate hybrid multicast/unicast 360° video delivery as a service-particularly combining LTE multicast and WiFi unicast transmissions. In addition we holistically define the emergent optimization problem, where we jointly consider network association, multicast/unicast scheduling, and spectrum/bandwidth management in both networks, taking also into consideration user specific preferences, e.g., the popularity of the video tiles. The proposed framework is evaluated through a series of simulation based experiments following 3GPP standard parameters and real 360° video traces. Our findings reveal significantly increase in the user utility, particularly when the wireless spectrum is limited.

In this paper we consider the task of scheduling packet transmissions amongst multiple paths with uncertain, time-varying delay. We make the observation that the requirement is usually to transmit application layer objects (web pages, images, video frames etc) with low latency, and so it is the object delay rather than the per packet delay which is important. This has fundamental implications for multipath scheduler design. We introduce SOS (Stochastic Object-aware Scheduler), the first multipath scheduler that considers application layer object sizes and their relationship to link uncertainty. We show how to interface SOS with cwnd-based/ack-clocked congestion control (as usually used in TCP) and show up to 150% improvement in the 95% percentile and mean delay vs MPTCP/minRTT.

Mobile network operators today need to deliver new types of multimedia content, such as 360° video, to an ever-growing population of users. This creates a pressing need for network mechanisms that can support such demanding services in an economically-efficient fashion. This work focuses on applications that deliver a video file concurrently to multiple users which can utilize different radio technologies, namely cellular and Wi-Fi networks. We propose a mechanism for the orchestration of LTE multicast and Wi-Fi unicast transmissions by optimizing jointly: the design of multicast groups, the spectrum and bandwidth management in both networks, and the video encoding quality for each user (or, group). We consider two key performance criteria: minimizing the utilized LTE spectrum resources, and maximizing the delivered video quality. We formulate the respective optimization problems, prove they are NP-hard, and design an exact algorithm for solving them in near-real time. We employ a wealth of simulation experiments, using 3GPP-compliant parameters, that compare our approach with state-of-the-art benchmarks. Our findings suggest that, in representative scenarios, this multi-RAT orchestration can save up 55% LTE radio resources and increase up to 46% the delivered video quality.

Wireless communications networks are operating at breaking point during an era of relentless traffic growth. Network operators must utilize scarce and expensive wireless spectrum efficiently in order to satisfy demand. Spectrum on the links between cells and user equipments ('users': smartphones, tablets, etc.) frequently becomes congested. Capacity can be increased by transmitting data packets via multiple links. Packets can be routed through multiple Long Term Evolution (LTE) links in existing fourth generation (4G) networks. In future 5G deployments, users will be equipped to receive packets over LTE, WiFi, and millimetre wave links simultaneously.

In the last decades, technological developments in wireless communications have been coupled with an increasing demand of mobile services. From real-time applications with focus on entertainment (e.g., high quality video streaming, virtual and augmented reality), to industrial automation and security scenarios (e.g., video surveillance), the requirements are constantly pushing the limits of communication hardware and software. Communications at millimeter wave frequencies could provide very high throughput and low latency, thanks to the large chunks of available bandwidth, but operating at such high frequencies introduces new challenges in terms of channel reliability, which eventually impact the overall end-to-end performance. In this paper, we introduce a proxy that coordinates the physical and transport layers to seamlessly adapt to the variable channel conditions and avoid performance degradation (i.e., latency spikes or low throughput). We study the performance of the proposed solution using a simulated IEEE 802.11ad-compliant network, with the integration of input traces generated from measurements from real devices, and show that the proposed proxy-based mechanism reduces the latency by up to 50% with respect to TCP CUBIC on a 60 GHz link.

Interactive cloud-based applications such as virtual reality telepresence require flawless data delivery well within interactivity deadlines. These can range from sub-ms intervals for machines-type communications in industrial settings (e.g., multi-view obstacle detection for mobile vehicles) to 100 ms for basic human-human interactivity (e.g., virtual office, remote cockpit). Yet in wireless networks serving mobile users, the support of any quality-of-service (QoS) constraints is challenging due to their inherent instability and best-effort nature. In this context, we propose a novel multi-connectivity framework that allows federating multiple wireless links such as 4G LTE, 5G New Radio, Wi-Fi and WiGig in a simple and backward-compatible manner, and use them for multi-path data delivery with controlled end-to-end throughput, latency and reliability. The proposal is based on software-defined transport-layer proxies that can be deployed instantaneously and upgraded on-demand with no need for any handset or network modifications. As a proof of concept, we evaluate several innovative protocols for user-centric bandwidth and latency control in real-life LTE and Wi-Fi networks.

Evolutionary computation is used to automatically evolve small cell schedulers on a realistic simulation of a 4G-LTE heterogeneous cellular network. Evolved schedulers are then further augmented by human design to improve robustness. Extensive analysis of evolved solutions and their performance across a wide range of metrics reveals evolution has uncovered a new human-competitive scheduling technique which generalises well across cells of varying sizes. Furthermore, evolved methods are shown to conform to accepted scheduling frameworks without the evolutionary process being explicitly told the form of the desired solution. Evolved solutions are shown to out-perform a human-engineered state-of-the-art benchmark by up to 50%. Finally, the approach is shown to be flexible in that tailored algorithms can be evolved for specific scenarios and corner cases, allowing network operators to create unique algorithms for different deployments, and to postpone the need for costly hardware upgrades.

As the capacity and reliability of mobile networks increases, so does the demand for more responsive end-to-end services: applications such as augmented reality, live video conferencing, and smart or autonomous vehicles require reliable, throughput-intensive end-to-end communications with strict delay constraints. Only consistently reliable delivery of data flows well within human interactivity deadlines will enable a truly immersive user experience. To enable data delivery within pre-defined deadlines, controlled on demand by an application or its user, we propose and demonstrate a novel transport-layer protocol for explicit latency control called latency-controlled end-to-end aggregation protocol (LEAP). The LEAP splits a data flow with quality of service (QoS) constraints into multiple subflows that are delivered over multiple parallel links (e.g., Wi-Fi and LTE in a standard smartphone, WiGig, and 5G in the near future). The subflow data rates are set based on a novel proactive forecasting of the achievable channel capacity, subject to application-specific QoS constraints. Cross-path encoding and redundancy adaptation are then used to deliberately balance the trade-off between maximum throughput, required delay, and minimum reliability as function of application/user-specific input parameters. When compared to leading state-of-the-art transport protocols in live network experiments, LEAP exhibits a superior capacity to reliably provide a high and stable throughput with bounded latency, both in wired and wireless scenarios. The LEAP is also the first protocol to allow applications to explicitly set their priorities, giving them the freedom to set the operating point in the trade-off between throughput, latency, and reliability.

Traditional single-tiered wireless communications networks cannot scale to satisfy exponentially rising demand. Operators are increasing capacity by densifying their existing macro cell deployments with co-channel small cells. However, cross-tier interference and load balancing issues present new optimization challenges in channel sharing heterogeneous networks (HetNets). One-size-fits-all heuristics for allocating resources are highly suboptimal, but designingad hoccontrollers requires significant human expertise and manual fine-tuning. In this paper, a unified, flexible, and fully automated approach for end-to-end optimization in multi-layer HetNets is presented. A hill climbing algorithm is developed for reconfiguring cells in real time in order to track dynamic traffic patterns. Schedulers for allocating spectrum to user equipment are automatically synthesized using grammar-based genetic programming. The proposed methods for configuring the HetNet and scheduling in the time–frequency domain can addressad hocobjective functions. Thus, the operator can flexibly tune the tradeoff between peak rates and fairness. Far cell edge downlink rates are increased by up to 250% compared with non-adaptive baselines. Alternatively, peak rates are increased by up to 340%. The experiments illustrate the utility and future potential of natural computing techniques in software-defined wireless communications networks.

Small Cells are being deployed alongside pre-existing Macro Cells in order to satisfy demand during the current era of exponential growth in mobile traffic. Heterogeneous networks are economical because both cell tiers share the same scarce and expensive spectrum. However, customers at cell edges experience severe cross-tier interference in channel sharing Het-Nets, resulting in poor service quality. Techniques for improving fairness globally have been developed in previous works. In this paper, a novel method for service differentiation at the level of individual customers is proposed. The proposed algorithm redistributes spectrum on a millisecond timescale, so that premium customers experience minimum downlink rates exceeding a target threshold. System level simulations indicate that downlink rate targets of at least 1 [Mbps] are always satisfied under the proposed scheme. By contrast, naive scheduling achieves the 1 [Mbps] target only 83% of the time. Quality of service can be improved for premium customers without significantly impacting global fairness metrics. Flexible service differentiation will be key to effectively monetizing the next generation of 5G wireless communications networks.

Genetic Algorithms (GAs) have been shown to be a very effective optimisation tool on a wide variety of problems. However, they are not without their drawbacks. GAs require time to run, and evolve a bespoke solution to the desired problem in real time. This requirement can prove to be prohibitive in a high-frequency dynamic environment where on-line training time is limited. Neural Networks (NNs) on the other hand can be trained at length off-line, before being deployed on-line, allowing for fast generation of solutions on demand. This study presents a hybrid approach to time-frame scheduling in a high frequency domain. A GA approach is used to generate a dataset of optimised human-competitive solutions. Deep Learning is then deployed to extract the underlying model within the GA, enabling fast optimisation on unseen data. This hybrid approach allows for NNs to generate GA-quality schedules on-line, almost 100 times faster than running the GA.

Heterogeneous cellular networks are composed of macro cells (MCs) and small cells (SCs) in which all cells occupy the same bandwidth. Provision has been made under the third generation partnership project-long term evolution framework for enhanced intercell interference coordination (eICIC) between cell tiers. Expanding on previous works, this paper instruments grammatical genetic programming to evolve control heuristics for heterogeneous networks. Three aspects of the eICIC framework are addressed including setting SC powers and selection biases, MC duty cycles, and scheduling of user equipments (UEs) at SCs. The evolved heuristics yield minimum downlink rates three times higher than a baseline method, and twice that of a state-of-the-art benchmark. Furthermore, a greater number of UEs receive transmissions under the proposed scheme than in either the baseline or benchmark cases.

Network operators are struggling to cope with exponentially increasing demand. Capacity can be increased by densifying existing Macro Cell deployments with Small Cells. The resulting two-tiered architecture is known as a Heterogeneous Network or 'HetNet'. Significant inter-tier interference in channel sharing HetNets is managed by resource interleaving in the time domain. A key task in this regard is scheduling User Equipment to receive data at Small Cells. Grammar-based Genetic Programming (GBGP) is employed to evolve models that map measurement reports to schedules on a millisecond timescale. Two different fitness functions based on evaluative and instructive feedback are compared. The former expresses an industry standard utility of downlink rates. Instructive feedback is obtained by computing highly optimised schedules offline using a Genetic Algorithm, which then act as target semantics for evolving models. This paper also compares two schemes for mapping the GBGP parse trees to Boolean schedules. Simulations show that the proposed system outperforms a state of the art benchmark and is within 17% of the estimated theoretical optimum. The impressive performance of GBGP illustrates an opportunity for the further use of evolutionary techniques in software-defined wireless communications networks.

Control of data delivery latency in wireless mobile networks is an open problem due to the inherently unreliable and stochastic nature of wireless channels. This paper explores how the current best-effort throughput-oriented wireless services could be evolved into latency-sensitive enablers of new mobile applications such as remote 3D graphical rendering for interactive virtual/augmented-reality overlay. Assuming that the signal propagation delay and achievable throughput meet the basic latency requirements of the user application, we examine the idea of trading excess/federated bandwidth for the elimination of non-negligible data re-ordering delays, caused by temporal transmission failures and buffer overflows. The general system design is based on (i) spatially diverse delivery of data over multiple paths with uncorrelated outage likelihoods, and (ii) forward packet protection based on encoding redundancy that enables proactive recovery of lost or intolerably delayed data without end-to-end re-transmissions. Our analysis is based on traces of real-life traffic in live carrier-grade LTE networks.

In heterogeneous cellular networks for mobile communications, small cells are deployed within the coverage range of primary macro cells to provide for a localized capacity boost in traffic hotspots. Sharing of communication channels among the small-cell and macro-cell tiers is spectrally efficient, but causes failures of control signaling and data channels due to unmitigated co-channel interference. Consequently, the small-cell coverage range and capacity deteriorate. In Long Term Evolution (LTE) networks, the performance of control channels such as the physical downlink control channels (PDCCH) is of particular concern because they are protected only by simple interference averaging based on pseudo-random subcarrier allocation. Observing that the randomization algorithms are primarily seeded by the physical cell identifiers (PCIs) and cell radio network temporary identifier (C-RNTIs), we show that efficient interference-aware scheduling of control transmissions can be enabled by optimized allocation of PCIs, C-RNTIs and PDCCH resources. Simulations of a 3 GPP-compliant heterogeneous network show that the small-cell size can be doubled for a better macro-cell traffic offload by trading the number of active PDCCHs for a higher small-cell expansion bias. Alternatively, the small-cell PDCCH capacity can be at least tripled for high-load applications such as Voice over LTE by using selective macro-cell PDCCH muting.

Grammatical Evolution (GE) is applied to the problem of load balancing in heterogeneous cellular network deployments (HetNets). HetNets are multi-tiered cellular networks for which load balancing is a scalable means to maximise network capacity, assuming similar traffic from all users. This paper describes a proof of concept study in which GE is used in a genetic algorithm-like way to evolve constants which represent cell power and selection bias in order to achieve load balancing in HetNets. A fitness metric is derived to achieve load balancing both locally in sectors and globally across tiers. Initial results show promise for GE as a heuristic for load balancing. This finding motivates a more sophisticated grammar to bring enhanced Inter-Cell Interference Coordination optimisation into an evolutionary framework.

In heterogeneous cellular networks for mobile communications, small cells are deployed within the coverage range of primary macro cells to provide for a localized capacity boost in traffic hotspots. The sharing of communication channels among the small-cell and macro-cell tiers is spectrally efficient, but causes failures of control signaling and data channels due to unmitigated co-channel interference. Consequently, the small-cell coverage range and capacity is reduced. In LTE networks, the performance of some control channels (namely, PDCCH) is of particular concern because the LTE standard does not allow interference coordination in those, but only allows for interference averaging based on pseudo-random subcarrier allocation. Observing that the randomization algorithms are primarily seeded by the UE cell identifiers (the so-called C-RNTI), we show that simple C-RNTI optimization and selective scheduling can be used to enable efficient frequency-selective interferenceaware scheduling also in these control channels. If combined with our previously formulated concept of orthogonally-filled subframes [1], the control channel reliability and capacity can be significantly improved, especially under high cell-selection biases.

The operation of wireless cellular networks can be efficiently supported by secondary small cells that are deployed at traffic hotspots within the coverage area of the primary macro cells. To this end, we examine the conditions under which the secondary base stations can share the same communication channel with the primary base stations under the constraint of a predefined quality of service, typical for uplink communications. In particular, requiring a target signal-to-interference-and-noise ratio (SINR) for each transmission accommodated in a common interference-limited channel, we formally assess the impact of the secondary infrastructure in a W-CDMA system on the global achievability of the uplink target SINRs via distributed closed-loop power control. The effects of cell load and inter-cell coupling are distinguished. As a result, we define solutions to the problems of base station placement, cell coverage optimization, and target SINR adaptation in W-CDMA heterogeneous networks, as well as discuss their compatibility with standard downlink-oriented deployment guidelines. Analytical conclusions are validated numerically by 3GPP-compliant simulations of a W-CDMA heterogeneous network.

In heterogeneous cellular networks (HetNets) for mobile communications, small cells (SC) are deployed within the coverage range of primary macro cells (MC) to provide for a localized capacity boost in traffic hotspots. Focusing on LTE systems and co-channel SC deployments reusing the communication channels of the hosting MCs, we show that the performance of the LTE control signaling - the main performance bottleneck under inter-tier interference due to an inflexible and interference-unaware resource allocation - can be significantly improved by optimizing the seeds of the standardized pseudo-random mapping algorithms - the physical cell identity (PCI) and user radio network temporary identifier (RNTI). As a generalized result, we propose the standard-compliant concept of orthogonally-filled subframes (OFS) that employs power-control/blanking of UE-specific (PDCCH) channels to ensure co-existence in LTE HetNets. Simulations of a 3GPP-compliant HetNet deployed in Dublin show that compared to the standardized subframe-blanking approaches, configuring OFSs allows (i) extending the SC coverage range up to 3-times, i.e. to offload better the MC traffic, and/or (ii) increasing the control-channel capacity up to 8-times, e.g. to enable high-load voice-over-LTE applications.

The outdoor deployment of small cells (SCs) in heterogeneous cellular networks for mobile communications is advantageous from both the capacity and the power consumption point of view. In practice, however, the network operators are limited by the high costs associated with the provisioning of backhaul communication and power supply to the SCs. This paper addresses these operational challenges by using optical links at the same time for high-capacity backhaul and wireless power supply. In particular, we present an experimental design of an optical wireless link for energy harvesting (EH). Our system consists of (i) a high-brightness white light-emitting diode (WLED) with an output luminous flux of 2200 lm, and (ii) an amorphous silicon (a-Si) solar panel. The distance of the experimental link ranges from 0.5m to 5m. The visible-light EH from the solar panel is negatively affected by the Lambertian radiation pattern of the WLED. In order to increase the collimation of the source light, we use three optical elements - a spherical lens, a reflector, and a parabolic mirror. The latter is shown to achieve a gain of 28.4 dB in EH over a distance of 5 m compared to the non-collimated case.

The operation of wireless cellular networks can be efficiently supported by secondary small cells that are deployed on-demand in traffic hotspots within the coverage area of primary network cells. In view of the growing traffic demand and the limited spectrum available, the objective of this study is to examine the conditions under which the secondary base stations can share the same communication channel with the primary base stations under the constraint of a predefined quality of service, typical for uplink communications. In particular, considering a minimum required signal-to-interference-and-noise ratio (SINR) for each uplink transmission accommodated in a common interference-limited channel, we formally assess the impact of the secondary infrastructure on the global achievability of the target SINRs via distributed closed-loop power control. Both the effects of intra-cell load and inter-cell coupling are investigated with the aim of providing deployment guidelines for the secondary small-cell infrastructure. Analytical conclusions are validated numerically by 3GPP-compliant simulations of a network deployed in Dublin.

This study examines the trade-offs related to the problem of coverage auto-configuration of open access small cells (SC) that (i) are on-demand deployed in the macro-cells (MC) of a W-CDMA network, and (ii) operate in the same frequency channel as the hosting/neighboring MCs. The goal is to identify stable and practically detectable equilibrium states whose achievement can represent an optimality criterion for implementing automatic coverage adaptation of the SCs. It is shown that, on the downlink (DL), the offloading of MC hotspot users by a nearby SC improves the DL signal-to-interference-and-noise ratio (SINR) and the DL effective throughput of the MC users at the expense of the SC users. Similarly on the uplink (UL), the maximum achievable UL SINR of the SC (MC) users decreases (increases) with a growing SC coverage, but a distinct equilibrium state of the MC-vs-SC trade-off defined on the basis of the spectral radius of a network information matrix can be observed. However, the DL and UL equilibria do not generally coincide unless the MC and SC performance requirements are identical. This fact complicates autonomous optimum partitioning of the SC power budget between data transmissions and coverage-defining pilot signals. Numerical simulations are based on a 3GPP-compliant system model.

In heterogeneous cellular networks (HetNets) for mobile communications, small cells are deployed within the coverage range of primary macro cells to provide for a localized capacity boost in traffic hotspots. The sharing of communication channels among the macro-cell and the small-cell tiers is spectrally efficient but causes failures of control signaling in LTE networks due to unmitigated co-channel interference. Consequently, the small-cell coverage range and control channel capacity is reduced. The present study explores to what extent the control channel bottleneck in inter-tier co-existence can be eliminated by using orthogonalized scheduling of the user-specific physical downlink control channels (PDCCH). Implementation-wise, the optimization of PDCCH scheduling is closely related to the management of the cell radio network temporary identifiers (C-RNTIs) as these determine the PDCCH resource allocation. Simulations of a 3GPP-compliant HetNet in a Dublin scenario show that the small-cell size can be doubled for a better macro-cell traffic offload by trading the number of active PDCCHs for a higher small-cell expansion bias. Alternatively, the small-cell PDCCH capacity can be tripled for high-load applications such as Voice over LTE by using macro-cell PDCCH muting.

We propose distributed algorithms for real-time monitoring and admission control that allow base stations in heterogeneous wireless cellular networks to dynamically serve mobile users under the constraint of: 1) accommodating all active transmissions in a single shared channel; and 2) guaranteeing a minimum signal-to-interference-plus-noise ratio (SINR) to each served user. In particular, we develop distributed techniques for iterative real-time computation of the spectral radius of an unknown network matrix (often the Perron root of the matrix) that indicates the time-varying limits of power control stability, i.e., the limits of network capacity. Solely locally available information is used as algorithmic input. By drawing a formal analogy with the Google PageRank algorithm, the computations are shown analytically to be exponentially fast and sufficiently accurate for optimal (error-free) stability detection. Numerical simulations of an existing office building demonstrate the applicability of the proposed algorithms to actual UMTS W-CDMA systems characterized by discrete power control with limited step-size.

This paper investigates the effect of the other-cell interference in WCDMA small cell networks when operating on the same frequency channel of the hosting macrocell tier. In this paper, the problem formulation is presented and it is shown that the co-channel interference and the effective capacity can not be analytically traced. Consequently, using simulation and modelling, the paper investigates the interference exposure of the macrocell tier on the small cells in regards to the small cell's distance to the macrocell Base Station (BS), the small cell transmission power, the environment shadowing and macrocell load level. The results can serve as essential guidelines for operators when estimating the effective capacity of small cells in co-channel deployment scenarios.

In wireless networks, simultaneously active transmitters typically operate in separate communication channels to avoid mutual interference. This study focuses on the challenge of increasing the capacity of a wireless network by enabling multiple transmissions in each available channel. Active transmitters are assumed to maintain the receiver signal-to-noise-and-interference ratio (SINR) at a predetermined target value via power control to promote the quality of wireless connections. To this end, we propose distributed medium access algorithms that allow every transmitter-receiver pair to determine whether a target SINR is physically achievable through iterative power control in a given shared channel. The proposed algorithms are shown by theoretical analysis to be fast, accurate, and energy efficient. Numerical simulations demonstrate their ability to outperform related medium access schemes based on random access, carrier sensing, controlled power up, or invariant channel probing. Our major contribution consists of solving the open problem of accurate real-time computation of the spectral radius of an unknown network information matrix. This makes our framework applicable not only to testing target SINR achievability, but also to other aspects of wireless engineering such as energy efficiency, power control stability, and handover prioritization, in which knowledge of the spectral radius plays a key role.

This study addresses the general problem of efficient resource management in wireless networks with arbitrary time-varying topologies. Communication channels are assumed to generally accommodate multiple simultaneous transmissions. In this context, we focus our attention on the problem of distributed transmission power allocation and medium access by links (transmitter-receiver pairs) that require a guaranteed minimum signal-to-interference and noise ratio (SINR) at the receiver for a reliable data transfer. The design constraints for derived solutions consist of (i) a theoretically optimum performance, (ii) minimum complexity in implementation, and (iii) reliable feedback on target SINR feasibility to both active and inactive links. To this end, we propose adaptive algorithms that employ real-time tracking of the spectral radius of the Foschini-Miljanic matrix by means of distributed interference measurements. The algorithm design is characterized by an inherent resistant to the effects of stochastic radio propagation phenomena and an exponential convergence rate - a fact which we prove analytically. Numerical simulations confirm that our approach to admission control reaches the performance upper bounds of comparison algorithms that are based on random access, carrier-sensing, fixed channel probing, controlled power-up, or channel measurements.

The goal of this study is to define a scheme for fast and accurate medium access control (MAC) in distributed power-controlled wireless networks. The system model assumes (i) arbitrary topologies, (ii) arbitrary call arrival rates, (iii) multiple links transmitting simultaneously over shared interference-limited channels, and (iv) transmitters updating their powers to maintain a predefined signal-to-interference and noise ratio (SINR) at the receiver. Departing from our own theoretical framework on the computation of the dominant eigenvalue of the network information matrix, we discuss an accurate MAC scheme that uses interference measurements as its only decision-making input. By accuracy is meant that the MAC scheme grants channel access to all links with achievable target SINRs and rejects others. In contrary to other schemes, our approach allows once-admitted links to continuously monitor the achievability of their SINR targets without any overhead. However, the initial admission decision of passive links relies on energy-consuming channel probing, which can be protracted in networks with high channel reuse. While maintaining the accuracy and reliability, we reduce the overall call admission delay and energy usage of the MAC scheme by employing simple techniques for future prediction by data estimation/extrapolation. Power control based on Kalman filtration is suggested for noise suppression. Numerical simulations demonstrate the potential of the proposed scheme.

This paper offers a new insight to the fundamental problem of efficient admission control in arbitrary power-controlled wireless networks with an unknown call arrival distribution. Active transmitter-receiver pairs are assumed to (i) communicate simultaneously over shared channels, (ii) define target signal-to-interference and noise ratios (SINRs) by nonlinear functions of channel interference, and (iii) use adaptive power control to maintain the actual SINR at the target level in response to interference variations. Unlike other studies, in this study, power control with limited dynamic range and both the discrete-time and the continuous-time dynamics is explicitly considered, as well as the effects of stochastic radio propagation phenomena. Without relying on a priori assumptions, we first define sufficient conditions for a channel allocation mechanism to ensure the SINR constraints in cooperation with the deployed power control mechanism. We use the concept of Lyapunov stability as a cross-layer optimization criterion. Subsequently, we focus on the widely assumed case of SINR targets being defined by linear functions of interference, and show that such targets can be achieved if hii> |Ai|¿ j¿i hij¿i, where hijis the channel gain between the transmitter of link j and the receiver of link i, and Ai is the slope of the linear definition of the target SINR. This knowledge allows us to propose a simple distributed algorithm for implementing an admission control mechanism that (i) uses interference and pilot signal measurements as its only decision-making input, and (ii) allows links to adaptively adjust the SINR targets within the system stability bounds. This mechanism is shown to outperform the carrier sensing approach (CSMA/CA) for admission control.

An important performance measure in wireless networks is the manner in which the network can distributively manage its limited energy and spectrum resources, while assuring certain quality of service for communicating users. The current practice is to develop schemes with low complexity that are based on workable, but theoretically suboptimal techniques such as random access or carrier sensing. To address the need for equally simple, but optimally performing resource management schemes, we propose a set of optimum distributed algorithms for adaptive admission control and power control, which jointly (i) maximize the number of transmitters that transmit simultaneously in shared channel(s) with a guaranteed target signal-to-interference and noise ratio (SINR) at the receiver; (ii) minimize the transmit powers required to satisfy the SINR targets; (iii) use interference measurements as the only decision-making input; and (iv) provide inadmissible links with feedback on feasible SINR for (re)admission purposes. Unlike previous studies in which SINR targets were assumed as constants, we defined these targets using arbitrary linear functions of interference. From numerical simulations, it is confirmed that the proposed scheme outperforms other schemes by achieving the theoretical performance bounds.

Medium access control (MAC) represents a vital part of any wireless network and directly affects important quality-of-service (QoS) measures such as the end-to-end delay or data throughput. In this study, we evaluate the delay performance and robustness of a distributed adaptive MAC scheme for power-controlled wireless networks with shared communication channels. The scheme was proposed in our previous work and enables multiple network links (transmitter-receiver pairs) to transmit simultaneously over the shared channels with a guaranteed predefined signal-to-interference and noise ratio (SINR) at each receiver in order to achieve reliable data transfers. Moreover, in this scheme, links are admitted on the basis of theoretically optimum decisions and iterative interference measurements are the only algorithmic input. Our simulations show that, as a result of an exponential convergence rate, interference measurements in at most four power-update cycles are on average required to make optimum MAC decisions with probability one in networks with low and medium reuse of shared channels. Typically ten to twenty cycles are required in networks with higher channel congestion. Furthermore, if network links engage in collective MAC decision making by sharing information on measured co-channel interference, the overall robustness of the scheme to the effects of erroneous power control or channel impairments increases with the fraction of the participating links.

This paper analyzes distributed asynchronous power and rate control for wireless ad hoc networks. Importantly, all network transmitters are considered to be independent of any management infrastructure and to have the freedom to choose their own arbitrary control rules, using as input only information on local interference and achieved carrier signal-to-interference ratio (CIR). Such an approach respects diverse user preferences of on quality of service (QoS) and allows them to adapt to local network conditions in contrast with conventional cellular systems, whose users must follow centralized control commands from serving base stations. For this purpose, we develop a general non-cooperative game-theoretic framework and characterize the resulting power and rate allocation dynamics in terms of its convergence to network-wide acceptable equilibrium states under stochastic communication channels. Chief among the attractive features of our proposed framework is the fact that it is developed in an entirely abstract way without any particular technological or architectural assumptions, which are typically made in related works. Numerical simulations prove the potential of our approach to provide for fair, robust and comparably better CIR allocation in ad hoc networks with varying topology and user density.

This paper theoretically analyzes cross-layer optimized design of transmit power allocation in distributed interference-limited wireless networks with asynchronously acting links and stochastic communication channels, whereby the network link's architecture is abstracted into three layers - a physical, a data link and a network layer. We treat the transmit power allocation process as a result of coupled interaction, in which all the three layers try to satisfy their individual requirements on power control, admission control and routing respectively. Using a best-response approach for system modeling and the notion of network's stability for its cross-layer optimization, we present simple power control and admission control algorithms for convergent iterative allocation of equilibrium transmit powers, which optimally balance the network-wide trade-off between allocated transmit powers and resulting interference. Numerical simulations evaluate the achievable stability of our scheme with theoretical bounds.

This paper analyzes distributed asynchronous power and rate control for wireless ad hoc networks with stochastic channels. In contrast to conventional cellular systems, all network transmitters are assumed to be independent of any management infrastructure and, importantly, to have the freedom to choose their own arbitrary control rules, using as input only the information on local interference and achieved signal-to-interference and noise ratio (SINR). This approach respects link's different local network conditions and preferences on quality of service. With the purpose of finding network-wide acceptable equilibria for such an individually defined power/rate allocation dynamics, the authors discuss an entirely general asynchronous and distributed algorithm, whereby stochastic channels are assumed. Moreover, optimum admission scheme for linear/linearized models is given. Numerical simulations show the efficiency of our approach to allocate comparably higher SINRs in random ad hoc networks with changing topologies and user density.

This paper presents a novel game-theoretic framework for distributed adaptive power control in wireless ad-hoc networks. It first shows the equivalency of two recent approaches to this noncooperative field - the QoS utility maximization approach and the best-response approach. We then choose to follow the analytically more intuitive best-response approach and analyze in an abstract way general conditions for existence of optimal outcomes (Nash equilibria) of best-response power control dynamics. Consequently, we characterize conditions for global convergence to such states without any particular technical assumption. Our work provides a mathematically more general insight to game-theoretic power control compared to recent related works. Using the herein developed framework, we discuss CIR-based utility function maximization and show conditions for applicability of linear (linearized) best-response power control in connection with illustrative simulations

This paper presents a novel framework for distributed power control for ad-hoc wireless networks. We analyze dynamic adaptive power allocation assuming that transmit power is adjusted with respect to experienced interference based on general best-response functions. For this purpose, we develop a general non-cooperative game-theoretic framework in order to characterize optimal equilibrium states and convergence of distributed power control dynamics to such states. Our work provides a more general insight to game-theoretic power control compared to most of recent works in this field. Moreover, our framework is developed in an abstract way without any technical assumption on particular modulation, coding, QoS measure definition or network architecture. To demonstrate an application of our framework, we show that stable linear best-response power control converges exponentially to a unique Nash equilibrium for any initial condition, which we confirm by numerical simulations.